Liberal Democrat UBI Policy Paper: An Analysis¶
The Liberal Democrat Working Group in May 2021 published a new policy paper exploring possible universal basic income policies for the party’s future platform, without committing to any particular one.. This release was within the context of the September Liberal Democrat Conference vote to campaign for universal basic income in future elections. The findings from the Working Group’s paper were presented by Paul Noblet, the chair, in the recorded Social Liberal Forum event What Kind of UBI?, chaired by Jon Alexander, in which Jane Dodds (Leader of the Liberal Democrats in Wales), Christine Jardine (Liberal Democrat MP for Edinburgh West) and Max Ghenis (Founder of the UBI Center) gave first responses to the proposals.
The policy paper outlines four different UBI reforms, which are partly offset by reductions to the personal allowance and National Insurance thresholds, as well as treating the UBI as earned income for means tested benefits. Both of these recoup a significant amount of the UBI expenditure, but in all cases leave a net cost ranging between £13bn and £93bn per year. The UBI Center has reproduced these analyses using the open-sourced microsimulation model OpenFisca-UK in order to provide additional household-level information such as poverty effects, distributional changes and changes to marginal tax rates.
Policy outlines¶
The details of the four reforms are shown in the table below. The reforms are mainly funded by tax changes, and recover additional costs through the benefit system.
Tax changes¶
Two proposals are made to fund the UBI expenditure: reducing the Personal Allowance and reducing the National Insurance Primary Threshold. The Personal Allowance is a disregard for general income available to every individual with respect to Income Tax. In 2020, it had the value of £12,500 a year. For example, for an individual earning £22,500 a year, the Personal Allowance is deducted from taxable income to arrive at £10,000, which is taxed at the basic rate of 20%, resulting in a tax charge of £2,000. Reducing the Personal Allowance to £2,500 leaves the individual with £20,000 taxable income, resulting in a £4,000 Income Tax charge. The Personal Allowance is only reduced for working-age adults (those over State Pension Age are exempt). In the last three UBI policies, the Personal Allowance is reduced to £2,500, though in the first it is higher, at £4,0001.
National Insurance is a tax paid on employment earnings. There are both employee and employer contributions, but the tax change to NI only increases the employee-side contributions by reducing the Primary Threshold. The threshold is currently set at £184/week: earnings under this are not taxed, earnings above this but under the Upper Earnings Limit (£967/week) are taxed at 12%, and earnings over the UEL are taxed at 2%. Lowering the Primary Threshold raises taxes in a similar way to the Personal Allowance reduction, but is less progressive due to the difference between an allowance and a threshold. Lowering an allowance by £1 effectively moves all thresholds down by £1, but lowering a threshold only moves that specific threshold.
UBI and benefit changes¶
The universe to which the basic income is paid is proposed to be working-age adults - individuals aged between 18 (inclusive) and the State Pension age (exclusive). This takes the form of a weekly payment in the amounts detailed below. No immediate changes or abolitions of existing benefit programs are proposed in the UBI policies. However, UBI payments would be taken into account and treated as earnings when existing benefits are means-tested: for example, if a person receives £70/week from Universal Credit and then is paid £60/week in UBI, their Universal Credit payment would be reduced at the 63% taper (assuming work allowances, set amounts of income disregarded by means tests, are exhausted) to arrive at £32.20 (which, combined with the £60 UBI, results in £93.20 - an increase of 33%).
import pandas as pd
pd.DataFrame({
"UBI amount (£/week)": [45, 60, 75, 90],
"Personal Allowance (£/year)": [4000, 2500, 2500, 2500],
"NI Primary Threshold (£/week)": [90, 50, 50, 50],
"Eligible groups": ["Working-age adults"] * 4,
"UBI in benefit means tests": ["Included"] * 4
}).set_index("UBI amount (£/week)")
| Personal Allowance (£/year) | NI Primary Threshold (£/week) | Eligible groups | UBI in benefit means tests | |
|---|---|---|---|---|
| UBI amount (£/week) | ||||
| 45 | 4000 | 90 | Working-age adults | Included |
| 60 | 2500 | 50 | Working-age adults | Included |
| 75 | 2500 | 50 | Working-age adults | Included |
| 90 | 2500 | 50 | Working-age adults | Included |
Reform effects¶
All four of the reforms produce positive results on poverty, inequality and other social metrics. Their net costs range significantly: at the low end, the £45 UBI is similar in cost to Housing Benefit, and at the high end the £90 UBI is more similar to the State Pension. The below table shows the effects on the budget, as well as to poverty (before housing costs), deep poverty (defined at half the poverty line) and inequality (measured by the Gini coefficient of disposable income).
from openfisca_uk import Microsimulation
from reform import WA_adult_UBI, include_UBI_in_means_tests, set_PA, set_PA_for_WA_adults, set_PT, net_cost
import numpy as np
import pandas as pd
from tqdm import trange, tqdm
baseline = Microsimulation(year=2020)
funding = (set_PA_for_WA_adults(2500), set_PT(50), include_UBI_in_means_tests())
ubi_45 = (WA_adult_UBI(45 * 52), set_PA_for_WA_adults(4000), set_PT(90), include_UBI_in_means_tests())
ubi_60 = (WA_adult_UBI(60 * 52), *funding)
ubi_75 = (WA_adult_UBI(75 * 52), funding)
ubi_95 = (WA_adult_UBI(95 * 52), funding)
net_costs = []
names = ["UBI", "Lower PA", "Lower PT", "Reduced benefits"]
for i in range(len(ubi_60) + 1):
net_costs += [net_cost(baseline, Microsimulation(ubi_60[:i], year=2020))]
net_costs = np.array(net_costs)
resulting_costs = pd.Series(net_costs[1:] - net_costs[:-1])
resulting_costs = resulting_costs.apply(lambda x : round(x / 1e+9, 1))
reform_tax_revenue = -resulting_costs[1] - resulting_costs[2]
from openfisca_uk.api import *
def get_results(reform: Reform):
sim = Microsimulation(reform, year=2020)
cost = net_cost(baseline, sim)
ubi_cost = sim.calc("UBI").sum()
benefit_revenue = ubi_cost - reform_tax_revenue - cost
baseline_poverty = baseline.calc("in_poverty_bhc", map_to="person").mean()
baseline_deep_poverty = baseline.calc("in_deep_poverty_bhc", map_to="person").mean()
poverty_change = (sim.calc("in_poverty_bhc", map_to="person").mean() - baseline_poverty) / baseline_poverty
deep_poverty_change = (sim.calc("in_deep_poverty_bhc", map_to="person").mean() - baseline_deep_poverty) / baseline_deep_poverty
baseline_gini = baseline.calc("household_net_income", map_to="person").gini()
inequality_change = (sim.calc("household_net_income", map_to="person").gini() - baseline_gini) / baseline_gini
baseline_income = baseline.calc("household_net_income", map_to="person")
gain = sim.calc("household_net_income", map_to="person") - baseline_income
percent_winners = (gain > 0).mean()
percent_losers = (gain < 0).mean()
return sim, cost, poverty_change, deep_poverty_change, inequality_change, percent_winners, percent_losers, ubi_cost, benefit_revenue
reforms = (ubi_45, ubi_60, ubi_75, ubi_95)
names = ("£45", "£60", "£75", "£95")
results = list(map(get_results, reforms))
sims, costs, poverty_changes, deep_poverty_changes, inequality_changes, percent_winners, percent_losers, ubi_cost, benefit_revenue = list(zip(*results))
LD_lower = (13, 22, 48, 84)
LD_upper = (18, 28, 56, 93)
pd.DataFrame({
"UBI (£/week)": names,
"Net cost (£bn)": pd.Series(costs).apply(lambda x: round(x / 1e+9, 1)),
"Poverty change (%)": pd.Series(poverty_changes).apply(lambda x: round(x * 100, 1)),
"Deep poverty change (%)": pd.Series(deep_poverty_changes).apply(lambda x: round(x * 100, 1)),
"Inequality change (%)": pd.Series(inequality_changes).apply(lambda x : round(x * 100, 1)),
"Winners (%)": pd.Series(percent_winners).apply(lambda x : round(x * 100, 1)),
"Losers (%)": pd.Series(percent_losers).apply(lambda x : round(x * 100, 1))
}).set_index("UBI (£/week)")
| Net cost (£bn) | Poverty change (%) | Deep poverty change (%) | Inequality change (%) | Winners (%) | Losers (%) | |
|---|---|---|---|---|---|---|
| UBI (£/week) | ||||||
| £45 | 21.8 | -17.0 | -50.5 | -2.8 | 69.9 | 16.2 |
| £60 | 34.3 | -21.8 | -62.6 | -3.5 | 71.7 | 14.8 |
| £75 | 62.7 | -30.1 | -71.4 | -4.4 | 81.2 | 5.5 |
| £95 | 100.7 | -38.5 | -77.6 | -5.4 | 85.9 | 0.8 |
Funding breakdown¶
Below specifies the second reform, and calculates the effects of each component under the household-level simulation. This is a £60/week UBI for working-age adults, funded in part by including it in benefit means tests2, reducing the Personal Allowance for working-age adults and reducing the National Insurance Primary Threshold to £50/week. Overall, the Personal Allowance provides most of the funding, and the inclusion in means tested benefits provides only a small part that could be covered by reducing the personal allowance further.
pd.DataFrame({"Component": names, "Net cost (£bn)": resulting_costs, "Resulting net cost (£bn)": resulting_costs.cumsum()}).set_index("Component")
| Net cost (£bn) | Resulting net cost (£bn) | |
|---|---|---|
| Component | ||
| £45 | 126.9 | 126.9 |
| £60 | -60.7 | 66.2 |
| £75 | -19.1 | 47.1 |
| £95 | -12.8 | 34.3 |
Distributional effects¶
Inter-decile effects¶
The above effects only show the picture on the population as a whole. Below shows the average gain to members of the population by their household’s equivalised disposable income. With deficit funding, the plans are able to show positive impacts on average for every decile in every reform. Raising taxes to fund these proposals would likely create average losses for the higher deciles if the tax rises are progressive.
import plotly.graph_objects as go
from ubicenter import format_fig
fig = go.Figure()
income = baseline.calc("equiv_household_net_income", map_to="person")
for sim, name in zip(sims, names):
gain = sim.calc("household_net_income", map_to="person") - baseline.calc("household_net_income", map_to="person")
result = gain.groupby(income.decile_rank()).mean()
fig.add_trace(go.Bar(x=result.index, y=result.values, name=name))
format_fig(fig.update_layout(title="Gains by income decile", xaxis_tickvals=list(range(1, 11)), xaxis_title="Equivalised disposable income decile", yaxis_title="Absolute gain per year", yaxis_tickprefix="£"))
Intra-decile effects¶
There is some heterogeneity within the deciles, though not losses tend to be small. While the gains at the bottom of the income distribution are typically strong, the reforms are not particularly punitive on the top deciles either, which is to be expected given that the tax rises are limited to lower sections of income and, though to a smaller effect, changing the Personal Allowance explicitly does not affect those earning over £125,000 as it is phased out by this point. Below shows the distribution of outcomes for each decile, for each reform, including all individuals in the population.
import plotly.express as px
from charts import intra_decile_graph_data
intra = intra_decile_graph_data(baseline, *sims)
COLORS = [
"#9E9E9E",
"#E0E0E0",
"#444444",
"#C5E1A5",
"#558B2F",
]
format_fig(px.bar(intra, x="fraction", y="decile", orientation="h", color="band", animation_frame="UBI", color_discrete_sequence=COLORS).update_layout(
yaxis_tickvals=list(range(1, 11)),
xaxis_tickformat="%",
yaxis_title="Income decile",
xaxis_title="Outcome distributions",
title="Intra-decile outcomes"
))
Individual effects¶
The above results show the impact on the population as it stands, but not the theoretical effects on household budgets. Below is shown the graph of household disposable income for a single person on Universal Credit.
from openfisca_uk import IndividualSim
def plot_budget(household_config, title):
baseline_policy = IndividualSim()
ubi_policy = IndividualSim(ubi_45)
for policy in (baseline_policy, ubi_policy):
household_config(policy)
policy.vary("employment_income")
fig = go.Figure()
fig.add_trace(go.Scatter(x=baseline_policy.calc("employment_income")[0], y=baseline_policy.calc("household_net_income")[0], name="Baseline"))
fig.add_trace(go.Scatter(x=baseline_policy.calc("employment_income")[0], y=ubi_policy.calc("household_net_income")[0], name="Reform"))
fig.add_trace(go.Scatter(x=baseline_policy.calc("employment_income")[0], y=baseline_policy.calc("tax")[0], name="Tax (Baseline)", visible="legendonly"))
fig.add_trace(go.Scatter(x=baseline_policy.calc("employment_income")[0], y=ubi_policy.calc("tax")[0], name="Tax (Reform)", visible="legendonly"))
fig.add_trace(go.Scatter(x=baseline_policy.calc("employment_income")[0], y=baseline_policy.calc("benefits")[0], name="Benefits (Baseline)", visible="legendonly"))
fig.add_trace(go.Scatter(x=baseline_policy.calc("employment_income")[0], y=ubi_policy.calc("benefits")[0], name="Benefits (Reform)", visible="legendonly"))
fig.add_trace(go.Scatter(x=baseline_policy.calc("employment_income")[0], y=ubi_policy.calc("UBI")[0], name="UBI (Reform)", visible="legendonly"))
from ubicenter import format_fig
return format_fig(fig.update_layout(
title=title,
yaxis_tickprefix="£",
xaxis_tickprefix="£",
yaxis_title="Yearly amount",
xaxis_title="Employment income",
))
def single_person_UC(sim):
sim.add_person(age=26, is_benunit_head=True, name="adult"),
sim.add_benunit(adults=["adult"], claims_UC=True),
sim.add_household(adults=["adult"])
plot_budget(single_person_UC, "Budget for a single adult on Universal Credit")
Appendix¶
Additional details from the simulation and policies are given below.
Costing estimates¶
The Working Group used different modelling methods than were used in this analysis, resulting in our analysis producing higher net costs than the Working Group found. This is primarily due to the fact that this analysis used a household simulation method, rather than administrative data from HMRC. This allows for finer levels of detail and distributional analysis, but high incomes and benefit receipts are under-reported, causing an underestimate of the revenue raised by the tax reforms. The details are shown in the table below.
LD_lower = (13, 22, 48, 84)
LD_upper = (18, 28, 56, 93)
pd.DataFrame({
"UBI (£/week)": names,
"LD net cost (£bn)": [f"{low}-{high}" for low, high in zip(LD_lower, LD_upper)],
"Simulated net cost (£bn)": pd.Series(costs).apply(lambda x: round(x / 1e+9, 1)),
"UBI cost": pd.Series(ubi_cost).apply(lambda x: round(x / 1e+9, 1)),
"Tax revenue": pd.Series([reform_tax_revenue] * len(ubi_cost)).apply(lambda x: round(x / 1e+9, 1)),
"Reduced benefits": pd.Series(benefit_revenue).apply(lambda x: round(x / 1e+9, 1)),
"Difference from central estimate (£bn)": [round((c - (low + high) / 2 * 1e+9) / 1e+9, 1) for c, low, high in zip(costs, LD_lower, LD_upper)],
}).set_index("UBI (£/week)")
| LD net cost (£bn) | Simulated net cost (£bn) | UBI cost | Tax revenue | Reduced benefits | Difference from central estimate (£bn) | |
|---|---|---|---|---|---|---|
| UBI (£/week) | ||||||
| £45 | 13-18 | 21.8 | 95.1 | 0.0 | 73.3 | 6.3 |
| £60 | 22-28 | 34.3 | 126.9 | 0.0 | 92.6 | 9.3 |
| £75 | 48-56 | 62.7 | 158.6 | 0.0 | 95.8 | 10.7 |
| £95 | 84-93 | 100.7 | 200.9 | 0.0 | 100.2 | 12.2 |
Budgets for other household types¶
Below is a selection of the theoretical effects on different household types.
def couple_UC(sim):
sim.add_person(age=26, is_benunit_head=True, name="adult"),
sim.add_person(age=27, is_benunit_head=False, name="adult2"),
sim.add_benunit(adults=["adult", "adult2"]),
sim.add_household(adults=["adult", "adult2"])
plot_budget(couple_UC, "Couple on Universal Credit")
def couple_children_UC(sim):
sim.add_person(age=26, is_benunit_head=True, name="adult"),
sim.add_person(age=27, name="adult2"),
sim.add_person(age=3, name="child"),
sim.add_person(age=4, name="child2"),
sim.add_benunit(adults=["adult", "adult2"], children=["child", "child2"], claims_UC=True, claims_child_benefit=True)
sim.add_household(adults=["adult", "adult2"], children=["child", "child2"])
plot_budget(couple_children_UC, "Couple with two children on Universal Credit")